Categorization, Analysis, and Visualization of Computer-Mediated Communication and Electronic Markets

Abstract

Computer mediated communication (CMC) and electronic markets have seen tremendous growth due to the fast propagation of the internet. In spite of the numerous benefits of electronic communication, it is not without its pitfalls. Two characteristics of computer mediated communication have proven to be particularly problematic: online anonymity and the enormity of data present in cyber communities.This dissertation follows the design science research paradigm in MIS, by addressing issues pertaining to the design and development of an important IT artifact capable of alleviating the two aforementioned CMC concerns. We present 8 essays related to the creation of CMC systems that can provide improved text analysis capabilities by incorporating a richer set of textual information types. Using Systemic Functional Linguistic Theory (SFLT) as a kernel theory, emphasis is placed on developing techniques for analyzing textual and ideational information. A rich set of features are used to represent textual (e.g., style, genres, social cues etc.) and ideational (topics, sentiments, affects, etc.) information. The research revolves around a core set of algorithms utilized for feature selection, categorization, analysis, and visualization of CMC text. The dissertation is arranged in three parts. The first two parts attempt to develop a set of features and techniques that can effectively represent textual and ideational information. In Chapters 2-5, we leverage information types related to the textual meta-function of SFLT for enhanced identity and institutional trust. Experiments are conducted on various CMC modes prevalent in organizational settings, including email, instant messaging, forums, program code, and websites. Chapters 6-8 consider two important information types associated with the ideational meta-function of SFLT: opinions and emotions. We assess the ability to gauge consumer sentiments and affects using machine learning techniques on various CMC modes, including product review and social discussion forums.The third part relates to the design, development, and evaluation of a visualization system that can analyze the presence of the aforementioned information types in text-based CMC archives (Chapter 9). We propose a design framework for CMC text analysis systems that is grounded in SFLT. The CyberGate system is developed as an instantiation of the design framework.